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Python Data Science Handbook : Essential Tools for Working with Data Jacob T. Vanderplas; Jake VanderPlas Sebastopol, CA: O'Reilly Media, Inc., O'Reilly Media, Sebastopol, CA, 2016

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them allIPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, youll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

English [en] · PDF · 22.6MB · 2016 · 📗 Book (unknown) · 🚀/ia · Save

lgli/Плас - Python для сложных задач. Наука о данных и машинное обучение - 2018.pdf

Python для сложных задач: наука о данных и машинное обучение: 16+ Дж. Вандер Плас; [перевела с английского И. Пальти] Питер, Бестселлеры O'Reilly, Санкт-Петербург [и др.], Russia, 2018

Книга «Python Data Science Handbook» - это подробное руководство по самым разным вычислительным и статистическим методам, без которых немыслима любая интенсивная обработка данных, научные исследования и передовые разработки. Читатели, уже имеющие опыт программирования и желающие эффективно использовать Python в сфере Data Science, найдут в этой книге ответы на всевозможные вопросы, например: 1) как мне считать этот формат данных в мой скрипт? 2) Как преобразовать, очистить эти данные и манипулировать ими? 3) Как визуализировать данные такого типа? Как при помощи этих данных разобраться в ситуации, получить ответы на вопросы, построить статистические модели или реализовать машинное обучение? https://github.com/jakevdp/PythonDataScienceHandbook

English [en] · Russian [ru] · PDF · 13.9MB · 2018 · 📘 Book (non-fiction) · 🚀/lgli/lgrs · Save

lgli/VanderPlas, Jake & chenjin5.com - Python Data Science Handbook: Essential Tools for Working with Data (2016, cj5_9678).mobi

Python Data Science Handbook : Essential Tools for Working with Data VanderPlas, Jake & chenjin5.com O'Reilly Media, Incorporated, 1st Edition, Dec 10, 2016

For Many Researchers, Python Is A First-class Tool Mainly Because Of Its Libraries For Storing, Manipulating, And Gaining Insight From Data. Several Resources Exist For Individual Pieces Of This Data Science Stack, But Only With The Python Data Science Handbook Do You Get Them All—ipython, Numpy, Pandas, Matplotlib, Scikit-learn, And Other Related Tools. Working Scientists And Data Crunchers Familiar With Reading And Writing Python Code Will Find This Comprehensive Desk Reference Ideal For Tackling Day-to-day Issues: Manipulating, Transforming, And Cleaning Data; Visualizing Different Types Of Data; And Using Data To Build Statistical Or Machine Learning Models. Quite Simply, This Is The Must-have Reference For Scientific Computing In Python.-- Ipython: Beyond Normal Python -- Introduction To Numpy -- Data Manipulation With Pandas -- Visualization With Matplatlib -- Machine Learning. Jake Vanderplas. Includes Index.

English [en] · MOBI · 9.4MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

lgli/Jake Vanderplas - Python Data Science Handbook: Essential Tools for Working With Data (O'Reilly Media, Incorporated).pdf

Python Data Science Handbook : Essential Tools for Working with Data Jacob T. Vanderplas; Jake VanderPlas O'Reilly Media, Incorporated, 1st Edition, Dec 10, 2016

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all--IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you'll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

English [en] · PDF · 15.9MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

nexusstc/Python Data Science Handbook: Essential Tools for Working with Data/696ac456dd05ac853f469fd205d847a9.pdf

Python Data Science Handbook: Essential Tools for Working with Data Jacob T. Vanderplas; Jake VanderPlas O'Reilly Media, Incorporated, O'Reilly Media, Sebastopol, CA, 2016

The Python Data Science Handbook provides a reference to the breadth of computational and statistical methods that are central to data-intensive science, research, and discovery. People with a programming background who want to use Python effectively for data science tasks will learn how to face a variety of problems: e.g., how can I read this data format into my script? How can I manipulate, transform, and clean this data? How can I visualize this type of data? How can I use this data to gain insight, answer questions, or to build statistical or machine learning models? This book is a reference for day-to-day Python-enabled data science, covering both the computational and statistical skills necessary to effectively work with . The discussion is augmented with frequent example applications, showing how the wide breadth of open source Python tools can be used together to analyze, manipulate, visualize, and learn from data.

English [en] · PDF · 7.6MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/Jake VanderPlas - Python Data Science Handbook: Essential Tools for Working with Data (2016, O’Reilly Media, Inc.).pdf

Python Data Science Handbook : Essential Tools for Working with Data Jacob T. Vanderplas; Jake VanderPlas O'Reilly Media, Incorporated, 1st Edition, Dec 10, 2016

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them allIPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, youll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

English [en] · PDF · 9.3MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

lgli/Jake VanderPlas [Jake VanderPlas] - Python Data Science Handbook (2016, O'Reilly Media, Inc.).epub

Python Data Science Handbook : Essential Tools for Working with Data Jake VanderPlas [Jake VanderPlas] O'Reilly Media, Incorporated, O'Reilly Media, Sebastopol, CA, 2016

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them allIPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, youll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

English [en] · EPUB · 14.5MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

nexusstc/Python Data Science Handbook/4943671270d6de48845d38cd01e90fe8.pdf

Python Data Science Handbook : Essential Tools for Working with Data Jacob T. Vanderplas; Jake VanderPlas O'Reilly Media, Incorporated, O'Reilly Media, Sebastopol, CA, 2016

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them allIPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, youll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

English [en] · PDF · 6.6MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/Jake VanderPlas - Python Data Science Handbook (2016, ).pdf

Python Data Science Handbook : Essential Tools for Working with Data Jacob T. Vanderplas; Jake VanderPlas O'Reilly Media, Incorporated, O'Reilly Media, Sebastopol, CA, 2016

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them allIPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, youll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

English [en] · PDF · 22.3MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

zlib/no-category/Jake VanderPlas/Python Data Science Handbook: Essential Tools for Working with Data_110222670.pdf

Python Data Science Handbook : Essential Tools for Working with Data Jacob T. Vanderplas; Jake VanderPlas O'Reilly Media, Incorporated, 1, PS, 2017

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them allIPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, youll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

English [en] · PDF · 15.8MB · 2017 · 📗 Book (unknown) · 🚀/zlib · Save

lgli/VanderPlas, Jake & chenjin5.com - Python Data Science Handbook: Essential Tools for Working with Data (2016, cj5).mobi

Python Data Science Handbook : Essential Tools for Working with Data VanderPlas, Jake & chenjin5.com O'Reilly Media, Incorporated, 1st Edition, Dec 10, 2016

For Many Researchers, Python Is A First-class Tool Mainly Because Of Its Libraries For Storing, Manipulating, And Gaining Insight From Data. Several Resources Exist For Individual Pieces Of This Data Science Stack, But Only With The Python Data Science Handbook Do You Get Them All—ipython, Numpy, Pandas, Matplotlib, Scikit-learn, And Other Related Tools. Working Scientists And Data Crunchers Familiar With Reading And Writing Python Code Will Find This Comprehensive Desk Reference Ideal For Tackling Day-to-day Issues: Manipulating, Transforming, And Cleaning Data; Visualizing Different Types Of Data; And Using Data To Build Statistical Or Machine Learning Models. Quite Simply, This Is The Must-have Reference For Scientific Computing In Python.-- Ipython: Beyond Normal Python -- Introduction To Numpy -- Data Manipulation With Pandas -- Visualization With Matplatlib -- Machine Learning. Jake Vanderplas. Includes Index.

English [en] · MOBI · 9.4MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

nexusstc/Python Data Science Handbook: Tools and Techniques for Developers/188ba9e08ea107f8b1741b6d4f16f497.pdf

Python Data Science Handbook : Essential Tools for Working with Data Jacob T. Vanderplas; Jake VanderPlas O'Reilly Media, Incorporated, O'Reilly Media, Sebastopol, CA, 2016

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: \* IPython and Jupyter: provide computational environments for data scientists using Python \* NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python \* Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python \* Matplotlib: includes capabilities for a flexible range of data visualizations in Python \* Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

English [en] · PDF · 20.9MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/Jake VanderPlas - Python Data Science Handbook (2016, O'Reilly).pdf

Python Data Science Handbook : Essential Tools for Working with Data Jacob T. Vanderplas; Jake VanderPlas O'Reilly Media, Incorporated, O'Reilly Media, Sebastopol, CA, 2016

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them allIPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, youll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

English [en] · PDF · 17.2MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

lgli/VanderPlas, Jake - Python Data Science Handbook: Essential Tools for Working with Data (2016, O'Reilly Media).mobi

Python Data Science Handbook : Essential Tools for Working with Data Jacob T. Vanderplas; Jake VanderPlas O'Reilly Media, Incorporated, O'Reilly Media, Sebastopol, CA, 2016

For Many Researchers, Python Is A First-class Tool Mainly Because Of Its Libraries For Storing, Manipulating, And Gaining Insight From Data. Several Resources Exist For Individual Pieces Of This Data Science Stack, But Only With The Python Data Science Handbook Do You Get Them All—ipython, Numpy, Pandas, Matplotlib, Scikit-learn, And Other Related Tools. Working Scientists And Data Crunchers Familiar With Reading And Writing Python Code Will Find This Comprehensive Desk Reference Ideal For Tackling Day-to-day Issues: Manipulating, Transforming, And Cleaning Data; Visualizing Different Types Of Data; And Using Data To Build Statistical Or Machine Learning Models. Quite Simply, This Is The Must-have Reference For Scientific Computing In Python.-- Ipython: Beyond Normal Python -- Introduction To Numpy -- Data Manipulation With Pandas -- Visualization With Matplatlib -- Machine Learning. Jake Vanderplas. Includes Index.

English [en] · MOBI · 9.9MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

nexusstc/Python Data Science Handbook (Jupyter Notebook Version)/12ac00578cc7da0fe79971b408afb21c.pdf

Python Data Science Handbook (Jupyter Notebook Version) it-ebooks iBooker it-ebooks, it-ebooks-2017, 2017

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them allIPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, youll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

English [en] · PDF · 21.9MB · 2017 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

upload/bibliotik/P/Python Data Science Handbook - Jake VanderPlas.pdf

Python Data Science Handbook : Essential Tools for Working with Data VanderPlas, Jacob T O'Reilly Media, Incorporated, Sebastopol, CA, 2017

"For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all--IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you'll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms." -- Prové de Proquest

English [en] · PDF · 20.9MB · 2017 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

upload/duxiu_main2/【大学堂40T教程】等多个文件/【大学堂知识付费社群】/学院课程合集/【陆家嘴学堂】5套课程合集/【陆家嘴学堂】Python金融数据分析高级训练营/陆家嘴学堂Python金融数据分析课件和视频/第一课学习资料和课件/Jake VanderPlas-Python Data Science Handbook. Essential Tools for Working with Data-O'Reilly Media (2016).pdf

Python Data Science Handbook. Essential Tools for Working with Data Jacob T. Vanderplas; Jake VanderPlas O'Reilly Media, Incorporated, 1st Edition, Dec 10, 2016

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all--IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you'll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

English [en] · PDF · 20.9MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

upload/duxiu_main2/【大学堂图书馆】/【大学堂40T教程】等多个文件/【02】epubee全站/【19】/b7/PythonDataScienceHandbook_EssentialTools.epub.epub

Python Data Science Handbook : Essential Tools for Working with Data Jacob T. Vanderplas; Jake VanderPlas O'Reilly Media, Incorporated, 1st Edition, Dec 10, 2016

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms \*\* About the Author Jake VanderPlas is a long-time user and developer of the Python scientific stack. He currently works as an interdisciplinary research director at the University of Washington, conducts his own astronomy research, and spends time advising and consulting with local scientists from a wide range of fields.

English [en] · EPUB · 8.0MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

nexusstc/Python Data Science Handbook: Essential Tools for Working With Data/b6f6c5b455c5877e932f0993d9168e06.epub

Python Data Science Handbook : Essential Tools for Working with Data Jake Vanderplas [Vanderplas, Jake] O'Reilly Media, Incorporated, 1st Edition, Dec 10, 2016

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms \*\* About the Author Jake VanderPlas is a long-time user and developer of the Python scientific stack. He currently works as an interdisciplinary research director at the University of Washington, conducts his own astronomy research, and spends time advising and consulting with local scientists from a wide range of fields.

English [en] · EPUB · 6.4MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

nexusstc/Python Data Science Handbook: Essential Tools for Working With Data/0dc83aa87f6c9cbcdf330da68412b117.azw3

Python Data Science Handbook : Essential Tools for Working with Data Jacob T. Vanderplas; Jake VanderPlas O'Reilly Media, Incorporated, 1st Edition, Dec 10, 2016

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms \*\* About the Author Jake VanderPlas is a long-time user and developer of the Python scientific stack. He currently works as an interdisciplinary research director at the University of Washington, conducts his own astronomy research, and spends time advising and consulting with local scientists from a wide range of fields.

English [en] · AZW3 · 8.4MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/K:\!genesis\0day\kolxoz\81\Cs_Computer science\CsDm_Data mining\Vanderplas J. Python data science handbook (O'Reilly, 2016)(ISBN 9781491912058)(O)(548s)_CsDm_.pdf

Python Data Science Handbook : Essential Tools for Working with Data Jacob T. Vanderplas; Jake VanderPlas O'Reilly Media, Incorporated, 1st Edition, Dec 10, 2016

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms \*\* About the Author Jake VanderPlas is a long-time user and developer of the Python scientific stack. He currently works as an interdisciplinary research director at the University of Washington, conducts his own astronomy research, and spends time advising and consulting with local scientists from a wide range of fields.

English [en] · PDF · 10.5MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/F:\!upload\_books\Machine Learning for OpenCV.epub

Machine learning for OpenCV : advanced methods and deep learning Michael Beyeler Packt Publishing Limited, Бестселлеры O'Reilly, Санкт-Петербург [и др.], Russia, 2018

"A practical introduction to the world of machine learning and image processing using OpenCV and Python. Computer vision is one of today's most exciting application fields of Machine Learning, From self-driving cars to medical diagnosis, computer vision has been widely used in various domains. This course will cover essential concepts such as classifiers and clustering and will also help you get acquainted with neural networks and Deep Learning to address real-world problems. The course will also guide you through creating custom graphs and visualizations, and show you how to go from raw data to beautiful visualizations. By the end of this course, you will be ready to create your own ML system and will also be able to take on your own machine learning problems."--Resource description page. Read more... Abstract: "A practical introduction to the world of machine learning and image processing using OpenCV and Python. Computer vision is one of today's most exciting application fields of Machine Learning, From self-driving cars to medical diagnosis, computer vision has been widely used in various domains. This course will cover essential concepts such as classifiers and clustering and will also help you get acquainted with neural networks and Deep Learning to address real-world problems. The course will also guide you through creating custom graphs and visualizations, and show you how to go from raw data to beautiful visualizations. By the end of this course, you will be ready to create your own ML system and will also be able to take on your own machine learning problems."--Resource description page

English [en] · EPUB · 28.0MB · 2018 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

nexusstc/Python Data Science Handbook: Essential Tools for Working With Data/9ec841ba57279c375e7a49ceb5912dcb.pdf

Python Data Science Handbook : Essential Tools for Working with Data Jacob T. Vanderplas; Jake VanderPlas O'Reilly Media, Incorporated, 1st Edition, Dec 10, 2016

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms \*\* About the Author Jake VanderPlas is a long-time user and developer of the Python scientific stack. He currently works as an interdisciplinary research director at the University of Washington, conducts his own astronomy research, and spends time advising and consulting with local scientists from a wide range of fields.

English [en] · PDF · 11.8MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/Jake Vanderplas - Python Data Science Handbook (2016, 蓝星出版社_57).mobi

Python Data Science Handbook : Essential Tools for Working with Data Jacob T. Vanderplas; Jake VanderPlas O'Reilly Media, Incorporated, 1st Edition, Dec 10, 2016

For Many Researchers, Python Is A First-class Tool Mainly Because Of Its Libraries For Storing, Manipulating, And Gaining Insight From Data. Several Resources Exist For Individual Pieces Of This Data Science Stack, But Only With The Python Data Science Handbook Do You Get Them All—ipython, Numpy, Pandas, Matplotlib, Scikit-learn, And Other Related Tools. Working Scientists And Data Crunchers Familiar With Reading And Writing Python Code Will Find This Comprehensive Desk Reference Ideal For Tackling Day-to-day Issues: Manipulating, Transforming, And Cleaning Data; Visualizing Different Types Of Data; And Using Data To Build Statistical Or Machine Learning Models. Quite Simply, This Is The Must-have Reference For Scientific Computing In Python.-- Ipython: Beyond Normal Python -- Introduction To Numpy -- Data Manipulation With Pandas -- Visualization With Matplatlib -- Machine Learning. Jake Vanderplas. Includes Index.